Nonlinear Optimal Tracking With Incomplete State Information Using State Dependent Riccati Equation

Ahmed Khamis, D. Subbaram Naidu, Dawid Zydek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A number of computational techniques have been offered for estimation of unmeasured states in nonlinear systems. Most of these techniques rely on applying the linear estimation techniques to the linearized systems, which can be effective only in the neighborhood of the operating point. This paper presents an online technique for nonlinear stochastic tracking problems. The idea of the proposed technique is to integrate the Kalman filter algorithm and the State Dependent Riccati Equation (SDRE) technique. Unlike the ordinary methods which deal with the linearized system, this technique will estimate the unmeasured states of the nonlinear system directly, and this will make the proposed technique effective for wide range of operating points. Numerical example is given to illustrate the effectiveness of the proposed technique.

Original languageEnglish (US)
Title of host publicationProgress in Systems Engineering - Proceedings of the 23rd International Conference on Systems Engineering
PublisherSpringer- Verlag
Pages27-33
Number of pages7
ISBN (Print)9783319084213
DOIs
StatePublished - Jan 1 2015
Event23rd International Conference on Systems Engineering, ICSEng 2014 - Las Vegas, NV, United States
Duration: Aug 19 2014Aug 21 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1089
ISSN (Print)2194-5357

Other

Other23rd International Conference on Systems Engineering, ICSEng 2014
CountryUnited States
CityLas Vegas, NV
Period8/19/148/21/14

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Keywords

  • Kalman filter
  • State Dependent Riccati Equation
  • nonlinear tracking
  • optimal control

Cite this

Khamis, A., Naidu, D. S., & Zydek, D. (2015). Nonlinear Optimal Tracking With Incomplete State Information Using State Dependent Riccati Equation. In Progress in Systems Engineering - Proceedings of the 23rd International Conference on Systems Engineering (pp. 27-33). (Advances in Intelligent Systems and Computing; Vol. 1089). Springer- Verlag. https://doi.org/10.1007/978-3-319-08422-0_4